{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPU: 32*40 in 27.9s = 46/s\n",
    "# CPU: 32*8 in 70s = 4/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using CNTK backend\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OS:  linux\n",
      "Python:  3.5.2 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:53:06) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "Numpy:  1.13.3\n",
      "Keras:  2.0.9\n",
      "CNTK:  2.2\n",
      "Keras using cntk\n",
      "Keras channel ordering is channels_last\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/py35/lib/python3.5/site-packages/keras/backend/cntk_backend.py:18: UserWarning: CNTK backend warning: GPU is not detected. CNTK's CPU version is not fully optimized,please run with GPU to get better performance.\n",
      "  'CNTK backend warning: GPU is not detected. '\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# TOGGLE THIS (hard to kill CNTK):\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n",
    "# ...\n",
    "os.environ['KERAS_BACKEND'] = \"cntk\"\n",
    "import sys\n",
    "import numpy as np\n",
    "import keras as K\n",
    "import cntk as C\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "print(\"OS: \", sys.platform)\n",
    "print(\"Python: \", sys.version)\n",
    "print(\"Numpy: \", np.__version__)\n",
    "print(\"Keras: \", K.__version__)\n",
    "print(\"CNTK: \", C.__version__)\n",
    "print(\"Keras using {}\".format(K.backend.backend()))\n",
    "print(\"Keras channel ordering is {}\".format(K.backend.image_data_format()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\r\n"
     ]
    }
   ],
   "source": [
    "!cat /proc/cpuinfo | grep processor | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name\r\n",
      "Tesla K80\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi --query-gpu=gpu_name --format=csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set channels first (default)\n",
    "K.backend.set_image_data_format('channels_first')\n",
    "BATCH_SIZE = 32\n",
    "RESNET_FEATURES = 2048\n",
    "BATCHES_GPU = 40\n",
    "BATCHES_CPU = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def give_fake_data(batches):\n",
    "    \"\"\" Create an array of fake data to run inference on\"\"\"\n",
    "    np.random.seed(0)\n",
    "    dta = np.random.rand(BATCH_SIZE*batches, 224, 224, 3).astype(np.float32)\n",
    "    return dta, np.swapaxes(dta, 1, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yield_mb(X, batchsize):\n",
    "    \"\"\" Function yield (complete) mini_batches of data\"\"\"\n",
    "    for i in range(len(X)//batchsize):\n",
    "        yield i, X[i*batchsize:(i+1)*batchsize]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1280, 224, 224, 3) (1280, 3, 224, 224)\n"
     ]
    }
   ],
   "source": [
    "# Create batches of fake data\n",
    "fake_input_data_cl, fake_input_data_cf = give_fake_data(BATCHES_GPU)\n",
    "print(fake_input_data_cl.shape, fake_input_data_cf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_fn(classifier, data, batchsize):\n",
    "    \"\"\" Return features from classifier \"\"\"\n",
    "    out = np.zeros((len(data), RESNET_FEATURES), np.float32)\n",
    "    for idx, dta in yield_mb(data, batchsize):\n",
    "        out[idx*batchsize:(idx+1)*batchsize] = classifier.predict_on_batch(dta).squeeze()\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download Resnet weights\n",
    "model = ResNet50(include_top=False, input_shape=(3,224,224))\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_input_data_cf = np.ascontiguousarray(fake_input_data_cf)\n",
    "cold_start = predict_fn(model, fake_input_data_cf, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24.9 s, sys: 1.17 s, total: 26.1 s\n",
      "Wall time: 27.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# GPU: 27.9s\n",
    "features = predict_fn(model, fake_input_data_cf, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. CPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to restart notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kill all GPUs ...\n",
    "# Done at top of script on re-run (before loading CNTK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ResNet50(include_top=False, input_shape=(3,224,224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 224, 224, 3) (256, 3, 224, 224)\n"
     ]
    }
   ],
   "source": [
    "# Create batches of fake data\n",
    "fake_input_data_cl, fake_input_data_cf = give_fake_data(BATCHES_CPU)\n",
    "print(fake_input_data_cl.shape, fake_input_data_cf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_input_data_cf = np.ascontiguousarray(fake_input_data_cf)\n",
    "cold_start = predict_fn(model, fake_input_data_cf, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6min 11s, sys: 7.9 s, total: 6min 19s\n",
      "Wall time: 1min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# CPU: 70s\n",
    "features = predict_fn(model, fake_input_data_cf, BATCH_SIZE)"
   ]
  }
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